The clinical significance and economic burden of bicuspid aortic valve (BAV) disease justify the need for improved clinical guidelines and more robust therapeutic modalities. Recent advances in medical imaging have demonstrated the existence of altered hemodynamics in these patients. To identify hemodynamic biomarkers for BAV patients, we present a machine learning method consisting of a feature selection mechanism to classify healthy volunteers and BAV patients accurately.
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